# convex objective function with linear constraint

for a no convex objective function with linear constraint, a lagrangian relaxation should work?

convex objective function with linear constraint

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asked 2011-10-05 21:46:01 +0100 **

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for a no convex objective function with linear constraint, a lagrangian relaxation should work?

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if f(x1,x2,..,xn) is convex (or concave) then minimizing or maximizing subject to the linear constraint g(x1,x2,..,xn)=0 should be the same as minimizing or maximizing

F(x1,x2,...,xn,L) = f(x1,x2,...xn) + L*g(x1,x2,...,xn)

in the unconstrained sense. See "minimize?" for help. Convex functions (or concave) functions will have a unique minimum (or unique maximum), so everything should work out.

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Asked: ** 2011-10-05 21:46:01 +0100 **

Seen: **278 times**

Last updated: **Jul 03 '14**

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This question is fairly vague, which is presumably why someone downvoted it. Can you give a more explicit formulation of your question? For instance, are you asking how to find "Lagrangian relaxations" in Sage?